ImageProcessing1-Introduction 数字图像处理 英文版
图像处理Chap 1 Introduction
1.1 概念(3) What is Digital Image?
• 数字图像
一幅图像可以定义为
一 个 二 维 函 数 f(x,y) ， 空间坐标(x,y)上的值 f称为该点图像的灰 度。当(x,y)和幅值 f
为离散数值时，称
该图像为数字图像。
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1.1 概念(3) What is Digital Image Processing?
• 1981年，IBM公司推出个人计算机 • 20世纪80年代，出现了VLSL（very large scale integration） • 现在，已出现了ULSI（ultra large scale integration)
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1.2 The Origins of Digital Image Processing (5)
Ｘ射线管是带有阴极和阳极的 真空管。阴极加热释放自由电子， 这些电子以很高的速度向阳极流 动，当电子撞击一个原子核时， 能量被释放并形成Ｘ射线辐射。 Ｘ射线的能量由另一边的阳极 电压控制，而Ｘ射线的数量由施 加于阴极灯丝的电源控制。
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1.3.3 紫外波段成像 Imaging in the Ultraviolet Band
• Security and rights protection
– Encryption(编密码) and watermarking
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1.1 概念(2) Why Digital?
• “Exactness”
– Perfect reproduction without degradation – Perfect duplication of processing result •
第一台用于图像处理的大型计算机出现在20世纪60年代。
最新郑爱华《数字图像处理双语》教案-
安徽大学本科教学课程教案课程代码：ZX36225课程名称：数字图像处理（双语）授课专业：计算机科学与技术/网络工程授课教师：郑爱华职称/学位：讲师/博士开课时间：二○一六至二○一七学年第2学期第1次课程教学方案第4次课程教学方案第4次教学活动设计Single sensorInexpensive but slowRotation1.3.2Image Acquisition Using Sensor Strips≥ 4000 sensorsCAT, MRIFlat bedLinear motionSensor ring, 3D≥4000*4000 sensorsno motiontypical example: CCD (Charge Coupled Device)2.3.4 A Simple Image Formation ModelMost of the images in which we are interested in this book are monochromatic images, whose values are said to span the gray scale, as discussed in Section 2.2. When an image is generated from a physical process, its values are proportional to energy radiated by a physicalsource(e.g., electromagnetic waves).As a consequence, f(x, y)本次课主要介绍图像感知和获取的过程，包括：单个传感器、带状传感器、传感器阵列以及简单的图像形成模型，接着，介绍了图像取样和量化的过程，讲述了相关概念和表示方法。
第5次课程教学方案第5次教学活动设计第6次课程教学方案第6次教学活动设计3.2.4 Piecewise-Linear Transformation FunctionsAdvantage: the form of piecewise functions can be arbitrarily complex. Disadvantage: their specification requires considerably more user input.Local Enhancement第8次课程教学方案第8次教学活动设计本次课主要介绍了锐化空间滤波器的基础以及拉普拉斯算子和梯度法同时也介绍了梯度法。
数字图像处理(冈萨雷斯第三版)英文翻译PPT课件
necessary to transform ordinary images into digital images that the
computer can process. Today's digital cameras can directly convert
visual images into digital images. A digitห้องสมุดไป่ตู้l image, similar to raster
Digital Image Processing
Tianjin University of Technology and Education School of Electronic Engineering
2017
-
1
Synopsis
The contents of the eight weeks are as follows: The first、second chapter is the introduction and the
The objective world is a three-dimensional space, but the general image is two-dimensional. Two dimensional images inevitably lose part of the information in the process of reflecting the threedimensional world. Even recorded information can be distorted and even difficult to recognize objects. Therefore, it is necessary to recover and reconstruct information from images, and to analyze and extract mathematical models of images so that people can have a correct and profound understanding of what is recorded in the image. This process becomes the process of image processing.
ImageProcessing_Unit1_slides爱丁堡大学图像处理研究生课程课件
Image Processing - Unit 1
javier.escudero@
10
Institute for Digital Communications (IDCOM) – Image Processing
256 × 256 pixels 128 × 128 pixels
Fig. 1.6 in Petrou’s
Image Processing - Unit 1 javier.escudero@ 12
Institute for Digital Communications (IDCOM) – Image Processing
Institute for Digital Communications (IDCOM) – Image Processing
Unit 1 – Introduction to digital image processing
Dr Javier Escudero javier.escudero@ School of Engineering / University of Edinburgh
False contouring – Content effects
Fig. 1.6 in Petrou’s
Image Processing - Unit 1 javier.escudero@ 17
Institute for Digital Communications (IDCOM) – Image Processing
False contouring (I)
• Keeping the size of the image constant and reducing the number of grey levels (G=2m) produces false contouring • However, this effect depends on the contents of the image
Digital_Image_Processing_chapter1
1.1.1 The World of Signal
The world is filled with many kinds of signals. Be divided into：Continuous-time(연속) and discrete-time(이산); periodic(주기) signal and aperiodic(비 주기) signal ; deterministic(확정) signal and random(무작위)signal. Human being possess a natural signal processing system. Human visual system(HVS) plays an important role in navigation, identification, verification, gait, gesture,posture, communication...
1.2.1 Image Digitization(디지털)
Image Digitization
1.2.2 Image quality
• The quality of an image strongly depends on the number of samples and gray levels.
1.2.3 categorization
[1] Pixel operations • Depend on the Input on the pixel. Eg: Image inverting(반전). [2] Local (neighborhood) operations • Depend on the Input values in a neighborhood of that pixel. Eg: Edge detection(가장자리 검사)
数字图像处理基础知识image_process
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数字图像处理概论
图 3.3
对数坐标直方图，平滑前
图 3.4
对数坐标直方图，两次平滑后，阈值 = 104
在实际情况下，图像常受到噪声等因素的影响，为了便于判定谷底位置，必须对直 方图进行平滑。采用低通滤波算法对直方图进行两次平滑，这样可以保证峰值位置误差 较小。图 3.4 为滤波后的结果。 图 3.5 是根据图 3.4 的阈值对图 3.1 进行阈值分割的结果。 即： 根据阈值将图像二值 化，将物体和背景置为黑白两色。对图像扫描一遍，灰度大于阈值的点置为 255，即白 色；小于等于阈值的点置为 0，即为黑色。 由于物体上有高光，所以二值化后，在黑色物体上会有小白点，如图 3.5 所示。为 了使形心计算的结果准确，我们必须将这些小白点填充为黑色。结果见图 3.6。
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数字图像处理概论
二. 图像的采集、格式
图像处理系统硬件结构：
计算机
物体
镜头
摄像头
模拟信号
图像采集卡
数字信号
NTSC 制、PAL 制 视频信号
内存
像素矩阵
0 0 0 图1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
256 级灰度 255
255 255 255 255 0
0 128 128 128 128 128 128
行
0
128 128 128 128 128 128
128
0 128 128 128 128 128 128 0 128 128 128 128 128 128 0 0 0 0 0 0 0
数字图像处理论文中英文对照资料外文翻译文献
第 1 页中英文对照资料外文翻译文献原 文To image edge examination algorithm researchAbstract ：Digital image processing took a relative quite young discipline,is following the computer technology rapid development, day by day obtains th widespread application.The edge took the image one kind of basic characteristic,in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widesp application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develo the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainlyhas Robert, Laplacian, Sobel, Canny, operators and so on LOG 。
数字图像处理冈萨雷斯课件英文Chapter01Eng绪论
Nighttime light of the world (cont.)
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Automated Visual Inspection
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Automated Visual Inspection (cont.)
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Digital Images in Early Era
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Digital Image Processing in Early Space Projects
Visible Light and Infrared
Cholesterol
Taxol
Microprocessor
Nickel oxide Thin film
Organic superconductor
?
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2nd Edition.
数字图像处理英文原版及翻译
Digital Image Processing and Edge DetectionDigital Image ProcessingInterest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception.An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pixels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields asingle number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in be- tween image processing and computer vision.There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and high level processes. Low-level processes involve primitive opera- tions such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higher level processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting(segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.”As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some form of organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnet- ic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to theother.Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. A familiar example of enhancement is when we increase the contrast of an image because “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good”enhancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it.Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image , such as the jpg used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a longway toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for trans- forming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital image processing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig 2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where theinformation of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig 2 above by the use of double-headed arrows between the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.Although point and line detection certainly are important in any discussion on segmentation,edge detection is by far the most common approach for detecting meaningful discounties in gray level.Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:1.focal blur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local specularities or interreflections in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there maytherefore usually be one edge on each side of the line.To illustrate why edge detection is not a trivial task, let us consider the problem of detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels.If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges.Hence, to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled.There are many methods for edge detection, but most of them can be grouped into two categories,search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian of the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking out irrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges.If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholdingparameters, and suitable thresholding values may vary over the image.Some edge-detection operators are instead based upon second-order derivatives of the intensity. This essentially captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.We can come to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that point.Since we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a threshold.Thus we define a point in an image as being as being an edge point if its two-dimensional first-order derivative is greater than a specified criterion of connectedness is by definition an edge.The term edge segment generally is used if the edge is short in relation to the dimensions of the image.A key problem in segmentation is to assemble edge segments into longer edges.An alternate definition if we elect to use the second-derivative is simply to define the edge ponits in an image as the zero crossings of its second derivative.The definition of an edge in this case is the same as above.It is important to note that these definitions do not guarantee success in finding edge in an image.They simply give us a formalism to look for them.First-order derivatives in an image are computed using the gradient.Second-order derivatives are obtained using the Laplacian.数字图像处理和边缘检测数字图像处理在数字图象处理方法的兴趣从两个主要应用领域的茎：改善人类解释图像信息;和用于存储，传输，和表示用于自主机器感知图像数据的处理。
数字图像处理(DigitalImageProcessing)
图像变换
傅里叶变换
将图像从空间域转换到频率域，便于分析图 像的频率成分。
离散余弦变换
将图像从空间域转换到余弦函数构成的系数 空间，用于图像压缩。
小波变换
将图像分解成不同频率和方向的小波分量， 便于图像压缩和特征提取。
沃尔什-哈达玛变换
将图像转换为沃尔什函数或哈达玛函数构成 的系数空间，用于图像分析。
理的自动化和智能化水平。
生成对抗网络（GANs）的应用
02
GANs可用于生成新的图像，修复老照片，增强图像质量，以及
进行图像风格转换等。
语义分割和目标检测
03
利用深度学习技术对图像进行语义分割和目标检测，实现对图
像中特定区域的识别和提取。
高动态范围成像技术
高动态范围成像（HDRI）技术
01
通过合并不同曝光级别的图像，获得更宽的动态范围
动态特效
数字图像处理技术可以用于制作动态特效，如电影、广告中的火焰、 水流等效果。
虚拟现实与增强现实
数字图像处理技术可以用于虚拟现实和增强现实应用中，提供更真 实的视觉体验。
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数字图像处理的未 来发展
人工智能与深度学习在数字图像处理中的应用
深度学习在图像识别和分类中的应用
01
利用深度学习算法，对图像进行自动识别和分类，提高图像处
医学影像重建
通过数字图像处理技术，可以将 CT、MRI等医学影像数据进行重建， 生成三维或更高维度的图像，便于 医生进行更深入的分析。
医学影像定量分析
数字图像处理技术可以对医学影像 进行定量分析，提取病变区域的大 小、形状、密度等信息，为医生提 供更精确的病情评估。
安全监控系统
视频监控
数字图像处理英文原版及翻译
数字图象处理英文原版及翻译Digital Image Processing: English Original Version and TranslationIntroduction:Digital Image Processing is a field of study that focuses on the analysis and manipulation of digital images using computer algorithms. It involves various techniques and methods to enhance, modify, and extract information from images. In this document, we will provide an overview of the English original version and translation of digital image processing materials.English Original Version:The English original version of digital image processing is a comprehensive textbook written by Richard E. Woods and Rafael C. Gonzalez. It covers the fundamental concepts and principles of image processing, including image formation, image enhancement, image restoration, image segmentation, and image compression. The book also explores advanced topics such as image recognition, image understanding, and computer vision.The English original version consists of 14 chapters, each focusing on different aspects of digital image processing. It starts with an introduction to the field, explaining the basic concepts and terminology. The subsequent chapters delve into topics such as image transforms, image enhancement in the spatial domain, image enhancement in the frequency domain, image restoration, color image processing, and image compression.The book provides a theoretical foundation for digital image processing and is accompanied by numerous examples and illustrations to aid understanding. It also includes MATLAB codes and exercises to reinforce the concepts discussed in each chapter. The English original version is widely regarded as a comprehensive and authoritative reference in the field of digital image processing.Translation:The translation of the digital image processing textbook into another language is an essential task to make the knowledge and concepts accessible to a wider audience. The translation process involves converting the English original version into the target language while maintaining the accuracy and clarity of the content.To ensure a high-quality translation, it is crucial to select a professional translator with expertise in both the source language (English) and the target language. The translator should have a solid understanding of the subject matter and possess excellent language skills to convey the concepts accurately.During the translation process, the translator carefully reads and comprehends the English original version. They then analyze the text and identify any cultural or linguistic nuances that need to be considered while translating. The translator may consult subject matter experts or reference materials to ensure the accuracy of technical terms and concepts.The translation process involves several stages, including translation, editing, and proofreading. After the initial translation, the editor reviews the translated text to ensure its coherence, accuracy, and adherence to the target language's grammar and style. The proofreader then performs a final check to eliminate any errors or inconsistencies.It is important to note that the translation may require adapting certain examples, illustrations, or exercises to suit the target language and culture. This adaptation ensures that the translated version resonates with the local audience and facilitates better understanding of the concepts.Conclusion:Digital Image Processing: English Original Version and Translation provides a comprehensive overview of the field of digital image processing. The English original version, authored by Richard E. Woods and Rafael C. Gonzalez, serves as a valuable reference for understanding the fundamental concepts and techniques in image processing.The translation process plays a crucial role in making this knowledge accessible to non-English speakers. It involves careful selection of a professional translator, thoroughunderstanding of the subject matter, and meticulous translation, editing, and proofreading stages. The translated version aims to accurately convey the concepts while adapting to the target language and culture.By providing both the English original version and its translation, individuals from different linguistic backgrounds can benefit from the knowledge and advancements in digital image processing, fostering international collaboration and innovation in this field.。
“数字图像处理技术”介绍概述PPT模板
之后十年
数字图像处理技术朝着更高深的方向发展，人们开始通过计算 机构建出数字化的人类视觉系统，这项技术被称为图像理解或 计算机视觉。
第二章 图像处理技术发展现状 ppt模板下载
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2.2 我国数字图像处理技术的发展
我国在建国之初就展开了计算机技术的研究，而改革开 放以来，我国在计算机数字图像处理技术上的发展进步也是 非常大的，甚至在某些理论研究方面已赶上了世界先进水平。
数字图像处理技术
Digital Image Processing
多媒体计算机技术
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CONTENTS PAGE
01 图像处理技术概述 02 图像处理技术发展现状 03 图像处理技术的利用
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0011 图图像像处处理理技技术术概概述述 02 图像处理技术发展现状 03 图像处理技术的利用
第三章 图像处理技术的利用
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谢谢
的比特量，这种技术现在的发展内容包括变换编码等，未来
可能发挥作用的还有小波变换图像压缩编码、分行编码等。
第三章 图像处理技术的利用
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3.2 图像处理技术的发展趋势
计算机数字图像处理技术在未来信息技术方面将会发 挥的重要作用早已被人们看到，对于计算机图像技术的发 展道路，大致可以归结出3个原则性内容：
①未来数据图像技术强调高清晰度、高速传输、实时图像处 理、三维成像或多维成像、智能化、自动化等方向发展。
②未来数字图像处理技术强调操作、运用的方便性，图像处 理功能的集中化趋势是必然会存在的。
③更新的理论研究与更快的算法研究。理论走在实践的前面， 已经是现代科学的特点，未来数字图像处理技术的实际运用 要取得更多的发展，必然离不开理论和研究方法的更新。
数字图像处理精确讲解(英文版)
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Lecture 7: Chapter 3 Spatial Domain Enhancement
Example to show the effects of different mask size on image appearance Technique: Excessive blurring is generally used to eliminate small objects in the image. They will be blended into the background of the image. The pronounced black border is the result of padding the border of the original image with 0’s (black) and then trimming off the padded area.
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Lecture 3: Chapter 2: Digital Image Fundamentals
数字图像处理概述【数字图像处理课程精品PPT】
Chen Duansheng
Digital Image Processing
Chapter 1: Introduction
Chen Duansheng
Digital Image Processing
Chapter 1: Introduction
Chen Duansheng
Digital Image Processing
Chen Duansheng
• X线胸透，大动脉的血管造影，头部CT， 电路版
• 天鹅座新星气云，X线|Gamma线|紫外sing
Chen Duansheng
正常玉米|患黑穗病玉米 的紫外线显微成像
Digital Image Processing
Chen Duansheng
胆固醇40|氧化镍薄膜600 |CD表面1750 的可见光显微成像
Digital Image Processing
Chen Duansheng
LANDSAT卫星多光谱成像，Washington D.C.
Digital Image Processing
Chen Duansheng
2. 《数字图像处理》第2版 R.C.冈萨雷斯,P.温茨著, 阮秋琦等译, 电子 工业出版社,2003（2002），￥59
3. 《数字图像处理》第3版 R.C.冈萨雷斯,P.温茨著, 阮秋琦等译, 电子 工业出版社,2011（2008），￥79
4. 《Digital Image Processing》3rd Edition. Rafael C. Gonzalez, Richard E. Woods,电子工业出版社2010-1 ￥79.8（影印）
Digital Image Processing
数字图像处理相关英文教材3
3HistogramsHistograms are used to depict image statistics in an easily interpreted visual format.With a histogram,it is easy to determine certain types of problems in an image,for example,it is simple to conclude if an image is properly exposed by visual inspection of its histogram.In fact,histograms are so useful that modern digital cameras often provide a real-time histogram overlay on the viewfinder(Fig.3.1)to help prevent taking poorly exposed pictures.It is important to catch errors like this at the image capture stage because poor exposure results in a permanent loss of information which it is not possible to recover later using image-processing techniques.In addition to their usefulness during image capture,histograms are also used later to improve the visual appearance of an image and as a“forensic”tool for determining what type of processing has previously been applied to an image.3.1What Is a Histogram?Histograms in general are frequency distributions,and histograms of images describe the frequency of the intensity values that occur in an image.This concept can be easily explained by considering an old-fashioned grayscale image like the one shown in Fig.3.2.A histogram h for a grayscale image I with intensity values in the range I(u,v)∈[0,K−1]would contain exactly K entries, where for a typical8bit grayscale image,K=28=256.Each individual histogram entry is defined ash(i)=the number of pixels in I with the intensity value i,W. Burger, M.J. Burge, Principles of Digital Image Processing, Undergraduate Topicsin Computer Science, DOI 10.1007/978-1-84800-191-6_3, Springer-Verlag London Limited, 2009©38 3.HistogramsFigure 3.1Digital camera back display showing a histogram overlay.Figure 3.2An 8-bit grayscale image and a histogram depicting the frequency distribution of its 256intensity values.for all 0≤i <K .More formally stated,h (i )=card (u,v )|I (u,v )=i .1(3.1)Therefore h (0)is the number of pixels with the value 0,h (1)the number of pixels with the value 1,and so forth.Finally h (255)is the number of all white pixels with the maximum intensity value 255=K −1.The result of the histogram computation is a one-dimensional vector h of length K .Figure 3.3gives an example for an image with K =16possible intensity values.Since a histogram encodes no information about where each of its individ-ual entries originated in the image,histograms contain no information about the spatial arrangement of pixels in the image.This is intentional since the 1card {...}denotes the number of elements (“cardinality”)in a set (see also p.233).3.2Interpreting Histograms 390123456789101112131415i i h (i h (i )Figure3.3Histogram vector for an image with K =16possible intensity values.The indices of the vector element i =0...15represent intensity values.The value of 10at index 2means that the image contains 10pixels of intensity value 2.Figure 3.4Three very different images with identical histograms.main function of a histogram is to provide statistical information,(e.g.,the distribution of intensity values)in a compact form.Is it possible to reconstruct an image using only its histogram?That is,can a histogram be somehow “in-verted”?Given the loss of spatial information,in all but the most trivial cases,the answer is no.As an example,consider the wide variety of images you could construct using the same number of pixels of a specific value.These images would appear different but have exactly the same histogram (Fig.3.4).3.2Interpreting HistogramsA histogram depicts problems that originate during image acquisition,such as those involving contrast and dynamic range,as well as artifacts resulting from image-processing steps that were applied to the image.Histograms are often used to determine if an image is making effective use of its intensity range (Fig.3.5)by examining the size and uniformity of the histogram’s distribution.40 3.HistogramsFigure3.5The effective intensity range.The graph depicts how often a pixel value occurs linearly(black bars)and logarithmically(gray bars).The logarithmic form makes even relatively low occurrences,which can be very important in the image,readily apparent. 3.2.1Image AcquisitionExposureHistograms make classic exposure problems readily apparent.As an example,a histogram where a large span of the intensity range at one end is largely unused while the other end is crowded with high-value peaks(Fig.3.6)is representative of an improperly exposed image.(a)(b)(c)Figure3.6Exposure errors are readily apparent in histograms.Underexposed(a),properly exposed(b),and overexposed(c)photographs.3.2Interpreting Histograms41(a)(b)(c)Figure 3.7How changes in contrast affect a histogram:low contrast(a),normal con-trast(b),high contrast(c).ContrastContrast is understood as the range of intensity values effectively used within a given image,that is the difference between the image’s maximum and minimum pixel values.A full-contrast image makes effective use of the entire range of available intensity values from a=a min...a max=0...K−1(black to white).Using this definition,image contrast can be easily read directly from the histogram.Figure3.7illustrates how varying the contrast of an image affects its histogram.Dynamic rangeThe dynamic range of an image is,in principle,understood as the number of distinct pixel values in an image.In the ideal case,the dynamic range encom-passes all K usable pixel values,in which case the value range is completely utilized.When an image has an available range of contrast a=a low...a high, witha min<a low and a high<a max,then the maximum possible dynamic range is achieved when all the intensity values lying in this range are utilized(i.e.,appear in the image;Fig.3.8).While the contrast of an image can be increased by transforming its existing values so that they utilize more of the underlying value range available,the dy-42 3.Histograms(a)(b)(c)Figure3.8How changes in dynamic range affect a histogram:high dynamic range(a), low dynamic range with64intensity values(b),extremely low dynamic range with only6 intensity values(c).namic range of an image can only be increased by introducing artificial(that is, not originating with the image sensor)values using methods such as interpola-tion(see Vol.2[6,Sec.10.3]).An image with a high dynamic range is desirable because it will suffer less image-quality degradation during image processing and compression.Since it is not possible to increase dynamic range after im-age acquisition in a practical way,professional cameras and scanners work at depths of more than8bits,often12–14bits per channel,in order to provide high dynamic range at the acquisition stage.While most output devices,such as monitors and printers,are unable to actually reproduce more than256dif-ferent shades,a high dynamic range is always beneficial for subsequent image processing or archiving.3.2.2Image DefectsHistograms can be used to detect a wide range of image defects that originate either during image acquisition or as the result of later image processing.Since histograms always depend on the visual characteristics of the scene captured in the image,no single“ideal”histogram exists.While a given histogram may be optimal for a specific scene,it may be entirely unacceptable for another. As an example,the ideal histogram for an astronomical image would likely be very different from that of a good landscape or portrait photo.Nevertheless,3.2Interpreting Histograms43 there are some general rules;for example,when taking a landscape image with a digital camera,you can expect the histogram to have evenly distributed intensity values and no isolated spikes.SaturationIdeally the contrast range of a sensor,such as that used in a camera,should be greater than the range of the intensity of the light that it receives from a scene. In such a case,the resulting histogram will be smooth at both ends because the light received from the very bright and the very dark parts of the scene will be less than the light received from the other parts of the scene.Unfortunately, this ideal is often not the case in reality,and illumination outside of the sensor’s contrast range,arising for example from glossy highlights and especially dark parts of the scene,cannot be captured and is lost.The result is a histogram that is saturated at one or both ends of its range.The illumination values lying outside of the sensor’s range are mapped to its minimum or maximum values and appear on the histogram as significant spikes at the tail ends.This typically occurs in an under-or overexposed image and is generally not avoidable when the inherent contrast range of the scene exceeds the range of the system’s sensor (Fig.3.9(a)).(a)(b)(c)Figure3.9Effect of image capture errors on histograms:saturation of high intensities(a), histogram gaps caused by a slight increase in contrast(b),and histogram spikes resulting from a reduction in contrast(c).Spikes and gapsAs discussed above,the intensity value distribution for an unprocessed image is generally smooth;that is,it is unlikely that isolated spikes(except for possible44 3.Histograms saturation effects at the tails)or gaps will appear in its histogram.It is also unlikely that the count of any given intensity value will differ greatly from that of its neighbors(i.e.,it is locally smooth).While artifacts like these are ob-served very rarely in original images,they will often be present after an image has been manipulated,for instance,by changing its contrast.Increasing the contrast(see Ch.4)causes the histogram lines to separate from each other and, due to the discrete values,gaps are created in the histogram(Fig.3.9(b)).De-creasing the contrast leads,again because of the discrete values,to the merging of values that were previously distinct.This results in increases in the corre-sponding histogram entries and ultimately leads to highly visible spikes in the histogram(Fig.3.9(c)).2Impacts of image compressionImage compression also changes an image in ways that are immediately evident in its histogram.As an example,during GIF compression,an image’s dynamic range is reduced to only a few intensities or colors,resulting in an obvious line structure in the histogram that cannot be removed by subsequent processing (Fig.3.10).Generally,a histogram can quickly reveal whether an image has ever been subjected to color quantization,such as occurs during conversion to a GIF image,even if the image has subsequently been converted to a full-color format such as TIFF or JPEG.Figure3.11illustrates what occurs when a simple line graphic with only two gray values(128,255)is subjected to a compression method such as JPEG, that is not designed for line graphics but instead for natural photographs.The histogram of the resulting image clearly shows that it now contains a large number of gray values that were not present in the original image,resulting ina poor-quality image3that appears dirty,fuzzy,and blurred.3.3Computing HistogramsComputing the histogram of an8-bit grayscale image containing intensity val-ues between0and255is a simple task.All we need is a set of256counters, one for each possible intensity value.First,all counters are initialized to zero. 2Unfortunately,these types of errors are also caused by the internal contrast“opti-mization”routines of some image-capture devices,especially consumer-type scan-ners.3Using JPEG compression on images like this,for which it was not designed,is one of the most egregious of imaging errors.JPEG is designed for photographs of natural scenes with smooth color transitions,and using it to compress iconic images with large areas of the same color results in strong visual artifacts(see,for example,Fig.1.9on p.19).3.3Computing Histograms45(a)(b)(c)Figure3.10Color quantization effects resulting from GIF conversion.The original image converted to a256color GIF image(left).Original histogram(a)and the histogram after GIF conversion(b).When the RGB image is scaled by50%,some of the lost colors are recreated by interpolation,but the results of the GIF conversion remain clearly visible in the histogram(c).(a)(b)(c)(d)Figure 3.11Effects of JPEG compression.The original image(a)contained only two different gray values,as its histogram(b)makes readily apparent.JPEG compression,a poor choice for this type of image,results in numerous additional gray values,which are visible in both the resulting image(c)and its histogram(d).In both histograms,the linear frequency(black bars)and the logarithmic frequency(gray bars)are shown.46 3.Histograms 1public class Compute_Histogram implements PlugInFilter{23public int setup(String arg,ImagePlus img){4return DOES_8G+NO_CHANGES;5}67public void run(ImageProcessor ip){8int[]H=new int[256];//histogram array9int w=ip.getWidth();10int h=ip.getHeight();1112for(int v=0;v<h;v++){13for(int u=0;u<w;u++){14int i=ip.getPixel(u,v);15H[i]=H[i]+1;16}17}18...//histogram H[]can now be used19}2021}//end of class Compute_HistogramProgram3.1ImageJ plugin for computing the histogram of an8-bit grayscale image.The setup()method returns DOES_8G+NO_CHANGES,which indicates that this plugin requires an8-bit grayscale image and will not alter it(line4).In Java,all elements of a newly instantiated array(line8)are automatically initialized,in this case to zero.Then we iterate through the image I,determining the pixel value p at each location(u,v),and incrementing the corresponding counter by one.At the end,each counter will contain the number of pixels in the image that have the corresponding intensity value.An image with K possible intensity values requires exactly K counter vari-ables;for example,since an8-bit grayscale image can contain at most256 different intensity values,we require256counters.While individual counters make sense conceptually,an actual implementation would not use K individ-ual variables to represent the counters but instead would use an array with K entries(int[256]in Java).In this example,the actual implementation as an array is straightforward.Since the intensity values begin at zero(like arrays in Java)and are all positive,they can be used directly as the indices i∈[0,N−1] of the histogram array.Program3.1contains the complete Java source code for computing a histogram within the run()method of an ImageJ plugin.At the start of Prog.3.1,the array H of type int[]is created(line8)and its elements are automatically initialized4to0.It makes no difference,at least in terms of thefinal result,whether the array is traversed in row or column 4In Java,arrays of primitives such as int,double are initialized at creation to0in the case of integer types or0.0forfloating-point types,while arrays of objects are initialized to null.3.4Histograms of Images with More than8Bits47 order,as long as all pixels in the image are visited exactly once.In contrast to Prog.2.1,in this example we traverse the array in the standard row-first order such that the outer for loop iterates over the vertical coordinates v and the inner loop over the horizontal coordinates u.5Once the histogram has been calculated,it is available for further processing steps or for being displayed.Of course,histogram computation is already implemented in ImageJ and is available via the method getHistogram()for objects of the class Image-Processor.If we use this built-in method,the run()method of Prog.3.1can be simplified topublic void run(ImageProcessor ip){int[]H=ip.getHistogram();//built-in ImageJ method...//histogram H[]can now be used}3.4Histograms of Images with More than8Bits Normally histograms are computed in order to visualize the image’s distribution on the screen.This presents no problem when dealing with images having 28=256entries,but when an image uses a larger range of values,for instance 16-and32-bit orfloating-point images(see Table1.1),then the growing number of necessary histogram entries makes this no longer practical.3.4.1BinningSince it is not possible to represent each intensity value with its own entry in the histogram,we will instead let a given entry in the histogram represent a range of intensity values.This technique is often referred to as“binning”since you can visualize it as collecting a range of pixel values in a container such as a bin or bucket.In a binned histogram of size B,each bin h(j)contains the number of image elements having values within the interval a j≤a<a j+1, and therefore(analogous to Eqn.(3.1))h(j)=card{(u,v)|a j≤I(u,v)<a j+1},for0≤j<B.(3.2) Typically the range of possible values in B is divided into bins of equal size k B=K/B such that the starting value of the interval j isa j=j·KB=j·k B.5In this way,image elements are traversed in exactly the same way that they are laid out in computer memory,resulting in more efficient memory access and with it the possibility of increased performance,especially when dealing with larger images (see also Appendix B,p.242).48 3.Histograms3.4.2ExampleIn order to create a typical histogram containing B =256entries from a 14-bitimage,you would divide the available value range if j =0...214−1into 256equal intervals,each of length k B =214/256=64,so that a 0=0,a 1=64,a 2=128,...a 255=16,320and a 256=a B =214=16,384=K .This results in the following mapping from the pixel values to the histogram bins h (0)...h (255):h (0)←0≤I (u,v )<64h (1)←64≤I (u,v )<128h (2)←128≤I (u,v )<192............h (j )←a j ≤I (u,v )<a j +1............h (255)←16320≤I (u,v )<163843.4.3ImplementationIf,as in the above example,the value range 0...K −1is divided into equal length intervals k B =K/B ,there is naturally no need to use a mapping table to find a j since for a given pixel value a =I (u,v )the correct histogram element j is easily computed.In this case,it is enough to simply divide the pixel value I (u,v )by the interval length k B ;that is,I (u,v )k B =I (u,v )K/B =I (u,v )·B K .(3.3)As an index to the appropriate histogram bin h (j ),we require an integer valuej = I (u,v )·B K,(3.4)where · denotes the floor function.6A Java method for computing histograms by “linear binning”is given in Prog.3.2.Note that all the computations from Eqn.(3.4)are done with integer numbers without using any floating-point op-erations.Also there is no need to explicitly call the floor function because the expressiona *B /Kin line 11uses integer division and in Java the fractional result of such an oper-ation is truncated,which is equivalent to applying the floor function (assuming3.5Color Image Histograms49 1int[]binnedHistogram(ImageProcessor ip){2int K=256;//number of intensity values3int B=32;//size of histogram,must be defined4int[]H=new int[B];//histogram array5int w=ip.getWidth();6int h=ip.getHeight();78for(int v=0;v<h;v++){9for(int u=0;u<w;u++){10int a=ip.getPixel(u,v);11int i=a*B/K;//integer operations only!12H[i]=H[i]+1;13}14}15//return binned histogram16return H;17}Program3.2Histogram computation using“binning”(Java method).Example of comput-ing a histogram with B=32bins for an8-bit grayscale image with K=256intensity levels. The method binnedHistogram()returns the histogram of the image object ip passed to it as an int array of size B.positive arguments).7The binning method can also be applied,in a similar way,tofloating-point images.3.5Color Image HistogramsWhen referring to histograms of color images,typically what is meant is a histogram of the image intensity(luminance)or of the individual color channels. Both of these variants are supported by practically every image-processing application and are used to objectively appraise the image quality,especially directly after image acquisition.3.5.1Intensity HistogramsThe intensity or luminance histogram h Lum of a color image is nothing more than the histogram of the corresponding grayscale image,so naturally all as-pects of the preceding discussion also apply to this type of histogram.The grayscale image is obtained by computing the luminance of the individual chan-nels of the color image.When computing the luminance,it is not sufficient to simply average the values of each color channel;instead,a weighted sum that 6 x rounds x down to the next whole number(see Appendix A,p.233).7For a more detailed discussion,see the section on integer division in Java in Ap-pendix B(p.237).50 3.Histograms takes into account color perception theory should be computed.This process is explained in detail in Chapter8(p.202).3.5.2Individual Color Channel HistogramsEven though the luminance histogram takes into account all color channels, image errors appearing in single channels can remain undiscovered.For ex-ample,the luminance histogram may appear clean even when one of the color channels is oversaturated.In RGB images,the blue channel contributes only a small amount to the total brightness and so is especially sensitive to this problem.Component histograms supply additional information about the intensity distribution within the individual color channels.When computing component histograms,each color channel is considered a separate intensity image and each histogram is computed independently of the other channels.Figure3.12 shows the luminance histogram h Lum and the three component histograms h R, h G,and h B of a typical RGB color image.Notice that saturation problems in all three channels(red in the upper intensity region,green and blue in the lower regions)are obvious in the component histograms but not in the lumi-nance histogram.In this case it is striking,and not at all atypical,that the three component histograms appear completely different from the correspond-ing luminance histogram h Lum(Fig.3.12(b)).3.5.3Combined Color HistogramsLuminance histograms and component histograms both provide useful informa-tion about the lighting,contrast,dynamic range,and saturation effects relative to the individual color components.It is important to remember that they pro-vide no information about the distribution of the actual colors in the image because they are based on the individual color channels and not the combi-nation of the individual channels that forms the color of an individual pixel. Consider,for example,when h R,the component histogram for the red channel, contains the entryh R(200)=24.Then it is only known that the image has24pixels that have a red intensity value of200.The entry does not tell us anything about the green and blue values of those pixels,which could be any valid value(∗);that is,(r,g,b)=(200,∗,∗).Suppose further that the three component histograms included the following entries:h R(50)=100,h G(50)=100,h B(50)=100.3.5Color Image Histograms 51(a)(b)h Lum(c)R (d)G (e)B(f)h R (g)h G (h)h BFigure 3.12Histograms of an RGB color image:original image (a),luminance histogram h Lum (b),RGB color components as intensity images (c–e),and the associated component histograms h R ,h G ,h B (f–h).The fact that all three color channels have saturation problems is only apparent in the individual component histograms.The spike in the distribution resulting from this is found in the middle of the luminance histogram (b).Could we conclude from this that the image contains 100pixels with the color combination(r,g,b )=(50,50,50)or that this color occurs at all?In general,no,because there is no way of ascertaining from these data if there exists a pixel in the image in which all three components have the value 50.The only thing we could really say is that the color value (50,50,50)can occur at most 100times in this image.So,although conventional (intensity or component)histograms of color im-ages depict important properties,they do not really provide any useful infor-mation about the composition of the actual colors in an image.In fact,a collection of color images can have very similar component histograms and still contain entirely different colors.This leads to the interesting topic of the com-bined histogram,which uses statistical information about the combined color components in an attempt to determine if two images are roughly similar in their color composition.Features computed from this type of histogram often52 3.Histograms form the foundation of color-based image retrieval methods.We will return to this topic in Chapter8,where we will explore color images in greater detail.3.6Cumulative HistogramThe cumulative histogram,which is derived from the ordinary histogram,is useful when performing certain image operations involving histograms;for in-stance,histogram equalization(see Sec.4.5).The cumulative histogram H isdefined asH(i)=ij=0h(j)for0≤i<K.(3.5)A particular value H(i)is thus the sum of all the values h(j),with j≤i,in the original histogram.Alternatively,we can define H recursively(as implemented in Prog.4.2on p.66):H(i)=h(0)for i=0H(i−1)+h(i)for0<i<K.(3.6)The cumulative histogram H(i)is a monotonically increasing function with amaximum valueH(K−1)=K−1j=0h(j)=M·N;(3.7)that is,the total number of pixels in an image of width M and height N.Figure 3.13shows a concrete example of a cumulative histogram.The cumulative histogram is useful not primarily for viewing but as a sim-ple and powerful tool for capturing statistical information from an image.In particular,we will use it in the next chapter to compute the parameters for several common point operations(see Sections4.4–4.6).3.7ExercisesExercise3.1In Prog.3.2,B and K are constants.Consider if there would be an advan-tage to computing the value of B/K outside of the loop,and explain your reasoning.Exercise3.2Develop an ImageJ plugin that computes the cumulative histogram of an 8-bit grayscale image and displays it as a new image,similar to H(i)in Fig.3.13.Hint:Use the ImageProcessor method int[]getHistogram()to retrieve3.7Exercises53iiH(i)Figure3.13The ordinary histogram h(i)and its associated cumulative histogram H(i).the original image’s histogram values and then compute the cumulative histogram“in place”according to Eqn.(3.6).Create a new(blank)image of appropriate size(e.g.,256×150)and draw the scaled histogram data as black vertical bars such that the maximum entry spans the full height of the image.Program3.3shows how this plugin could be set up and how a new image is created and displayed.Exercise3.3Develop a technique for nonlinear binning that uses a table of interval limits a j(Eqn.(3.2)).Exercise3.4Develop an ImageJ plugin that uses the Java methods Math.random()or Random.nextInt(int n)to create an image with random pixel values that are uniformly distributed in the range[0,255].Analyze the image’s his-togram to determine how equally distributed the pixel values truly are. Exercise3.5Develop an ImageJ plugin that creates a random image with a Gaussian (normal)distribution with mean valueμ=128and standard deviation σ=e the standard Java method double Random.nextGaussian() to produce normally-distributed random numbers(withμ=0andσ=1) and scale them appropriately to pixel values.Analyze the resulting image histogram to see if it shows a Gaussian distribution too.54 3.Histograms 1public class Create_New_Image implements PlugInFilter{2String title=null;34public int setup(String arg,ImagePlus im){5title=im.getTitle();6return DOES_8G+NO_CHANGES;7}89public void run(ImageProcessor ip){10int w=256;11int h=100;12int[]hist=ip.getHistogram();1314//create the histogram image:15ImageProcessor histIp=new ByteProcessor(w,h);16histIp.setValue(255);//white=25517histIp.fill();//clear this image1819//draw the histogram values as black bars in ip2here,20//for example,using histIp.putpixel(u,v,0)21//...2223//display the histogram image:24String hTitle="Histogram of"+title;25ImagePlus histIm=new ImagePlus(hTitle,histIp);26histIm.show();27//histIm.updateAndDraw();28}2930}//end of class Create_New_ImageProgram3.3Creating and displaying a new image(ImageJ plugin).First,we create a ByteProcessor object(histIp,line15)that is subsequentlyfilled.At this point,histIp has no screen representation and is thus not visible.Then,an associated ImagePlus object is created(line25)and displayed by applying the show()method(line26).Notice how the title(String)is retrieved from the original image inside the setup()method(line5)and used to compose the new image’s title(lines24and25).If histIp is changed after calling show(),then the method updateAndDraw()could be used to redisplay the associated image again(line27).。
Introduction_to_Digital_Image_Processing(翻译)-推荐下载
Introduction to Digital Image Processing数字图像处理7.1 What Is Digital Image Processing?7.1什么事图像处理f x y(,)An image may be defined as a two-dimensional function, where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of w hich has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.一幅图像可定义为一个二维函数f(x, y)，这里x和y是空间坐标，而在任何一对空间坐标f(x, y)上的幅值f称为该点图像的强度或灰度。
数字图像处理英文版
连续灰度的直方图规定
直方图匹配
令P(r) 为原始图象的灰度密度函 数，P(z)是期望通过匹配的图象灰 度密度函数。对P(r) 及P(z) 作直方 图均衡变换，通过直方图均衡为 桥梁，实现P(r) 与P(z) 变换。
直 方 图 匹 配
变 换 公 式 推 导 图 示
zk rj
直方图匹配
步骤： （1）由
Common quantization levels
f(ff fx,y) is given integer values [0-max], max=2n-1 n=1 n=5 (locally) n=8 n=16 n=24 images [0 1] [0 31] ”binary image” maximum the human eye can resolve
直方图均衡化变换公式推导图示
sj+s sj
rj
rj+r
直方图均衡化
考虑到灰度变换不影响象素的位 置分布，也不会增减象素数目。所 以有
r
0
p(r )dr p( s)ds 1 ds s T (r )
0 0
s
s
T (r ) p(r )dr
0
r
(2 1)
算例
nk 790 1023 850 656 329 245 122 81
p03 0.02
sk计算 sk舍入 0.19 1/7 0.44 3/7 0.65 5/7 0.81 6/7 0.89 6/7 0.95 1 0.98 1 1.00 1
sk计算 sk舍入 0.19 1/7 0.44 3/7 0.65 5/7 0.81 6/7 0.89 6/7 0.95 1 0.98 1 1.00 1
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Examples: The Hubble Telescope
Launched in 1990 the Hubble telescope can take images of very distant objects However, an incorrect mirror made many of Hubble’s images useless Image processing techniques were used to fix this
– New reproduction processes based on photographic techniques – Increased number of tones in reproduced images
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
– Image enhancement/restoration – Artistic effects – Medical visualisation – Industrial inspection – Law enforcement – Human computer interfaces
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1970s: Digital image processing begins to be used in medical applications
– 1979: Sir Godfrey N. Hounsfield & Prof. Allan M. Cormack share the Nobel Prize in medicine for the invention of tomography, the technology behind Computerised Axial Tomography (CAT) scans
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
Typical head slice CAT image
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History of DIP (cont…)
1980s - Today: The use of digital image processing techniques has exploded and they are now used for all kinds of tasks in all kinds of areas
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
A picture of the moon taken by the Ranger 7 probe minutes before landing
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History of DIP (cont…)
E-mail: Brian.MacNamee@dit.ie
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References
“Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Addison-Wesley, 2002
– Much of the material that follows is taken from this book
Improved digital image
Early 15 tone digital image
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History of DIP (cont…)
1960s: Improvements in computing technology and the onset of the space race led to a surge of work in digital image processing
Examples: Image Enhancement
One of the most common uses of DIP techniques: improve quality, remove noise etc
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
In this course we will stop here
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History of Digital Image Processing
Early 1920s: One of the first applications of digital imaging was in the newspaper industry
Low Level Process Input: Image Output: Image Examples: Noise removal, image sharpening Mid Level Process Input: Image Output: Attributes Examples: Object recognition, segmentation High Level Process Input: Attributes Output: Understanding Examples: Scene understanding, autonomous navigation
1 pixel
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
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What is a Digital Image? (cont…)
Common image formats include:
– 1 sample per point (B&W or Grayscale) – 3 samples per point (Red, Green, and Blue) – 4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a. Opacity)
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
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History of DIP (cont…)
Mid to late 1920s: Improvements to the Bartlane system resulted in higher quality images
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
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What is a Digital Image? (cont…)
Pixel values typically represent gray levels, colours, heights, opacities etc Remember digitization implies that a digital image is an approximation of a real scene
– The Bartlane cable picture transmission service Early digital image – Images were transferred by submarine cable between London and New York – Pictures were coded for cable transfer and reconstructed at the receiving end on a telegraph printer
“Machine Vision: Automated Visual Inspection and Robot Vision”, David Vernon, Prentice Hall, 1991
– Available online at: /rbf/BOOKS/VERNON/
Some argument about where image processing ends and fields such as image analysis and computer vision start
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What is DIP? (cont…)
The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes
Digital Image Processing: Introduction
Brian Mac Namee Brian.MacNamee@cபைடு நூலகம்mp.dit.ie
Course Website: p.dit.ie/bmacnamee
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Introduction
“One picture is worth more than ten thousand words” Anonymous
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Miscellanea
Lectures:
– Thursdays 12:00 – 13:00 – Fridays 15:00 – 16:00
Labs:
– Wednesdays 09:00 – 11:00
Web Site: p.dit.ie/bmacnamee/
– Previous year’s slides are available here – Slides etc will also be available on WebCT